OpenRaiser/Intern-Atlas huggingface dataset:
Reasoning Inconsistencies and How to Mitigate Them in Deep Learning
Venue: arXiv cs.AI 2025
Year: 2025

The recent advancements in Deep Learning models and techniques have led to significant strides in performance across diverse tasks and modalities. However, while the overall capabilities of models show promising growth, our understanding of their internal reasoning processes remains limited, particularly concerning systematic inconsistencies or errors patterns of logical or inferential flaws. These inconsistencies may manifest as contradictory outputs, failure to generalize across similar tasks, or erroneous conclusions in specific contexts. Even detecting and measuring such reasoning discrepancies is challenging, as they may arise from opaque internal procedures, biases and imbalances in training data, or the inherent complexity of the task. Without effective methods to detect, measure, and mitigate these errors, there is a risk of deploying models that are biased, exploitable, or logically unreliable. This thesis aims to address these issues by producing novel methods for deep learning models that reason over knowledge graphs, natural language, and images. The thesis contributes two techniques for detecting and quantifying predictive inconsistencies originating from opaque internal procedures in natural language and image processing models. To mitigate inconsistencies from biases in training data, this thesis presents a data efficient sampling method to improve fairness and performance and a synthetic dataset generation approach in low resource scenarios. Finally, the thesis offers two techniques to optimize the models for complex reasoning tasks. These methods enhance model performance while allowing for more faithful and interpretable exploration and exploitation during inference. Critically, this thesis provides a comprehensive framework to improve the robustness, fairness, and interpretability of deep learning models across diverse tasks and modalities.
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has recently shown strong potential in improving the reasoning capabilities of large language models and is now being actively extended to vision-language models (VLMs). However, existing RL applications in VLMs often rely on heavily engineered frameworks that hinder reproducibility and accessibility, while lacking standardized evaluation protocols, making it difficult to compare results or interpret training dynamics. This work introduces a transparent, from-scratch framework for RL in VLMs, offering a minimal yet functional four-step pipeline validated across multiple models and datasets. In addition, a standardized evaluation scheme is proposed to assess training dynamics and reflective behaviors. Extensive experiments on visual reasoning tasks uncover key empirical findings: response length is sensitive to random seeds, reflection correlates with output length, and RL consistently outperforms supervised fine-tuning (SFT) in generalization, even with high-quality data. These findings, together with the proposed framework, aim to establish a reproducible baseline and support broader engagement in RL-based VLM research.
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Venue: arXiv cs.LG 2025
Year: 2025

The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data. While the geometrical properties of such certificate functions are well understood, synthesizing them using machine learning techniques still remains a challenge. To mitigate this issue, we propose a diffeomorphic function learning framework where prior structural knowledge of the desired output is encoded in the geometry of a simple surrogate function, which is subsequently augmented through an expressive, topology-preserving state-space transformation. Thereby, we achieve an indirect function approximation framework that is guaranteed to remain in the desired hypothesis space. To this end, we introduce a novel approach to construct diffeomorphic maps based on RBF networks, which facilitate precise, local transformations around data. Finally, we demonstrate our approach by learning diffeomorphic Lyapunov functions from real-world data and apply our method to different attractor systems.
Instance-Optimal Imprecise Convex Hull
Venue: arXiv cs.CG 2025
Year: 2025

Imprecise measurements of a point set P = (p1, ..., pn) can be modelled by a family of regions F = (R1, ..., Rn), where each imprecise region Ri contains a unique point pi. A retrieval models an accurate measurement by replacing an imprecise region Ri with its corresponding point pi. We construct the convex hull of an imprecise point set in the plane, where regions in F may be retrieved at unit cost. The goal is to determine the cyclic ordering of the convex hull vertices of P as efficiently as possible. Here, efficiency is interpreted in two ways: (i) minimising the number of retrievals, and (ii) computing each retrieval location quickly. Prior works focused on only one of these two aspects: either minimising retrievals or optimising algorithmic runtime. Our contribution is the first to simultaneously achieve both. Let r(F, P) denote the minimal number of retrievals required by any algorithm to determine the convex hull of P for a given instance (F, P). For a family F of n constant-complexity polygons, our main result is a reconstruction algorithm that performs O(r(F, P)) retrievals in O(r(F, P) log^3 n) time. Compared to previous approaches that achieve optimal retrieval counts, we improve the runtime per retrieval by a exponential factor, from polynomial to polylogarithmic. Compared to near-linear time algorithms, we significantly reduce the number of retrievals used, and broaden the input families to include overlapping regions. We further extend our results to simple k-gons and to pairwise disjoint disks with radii in [1,k], where our runtime scales linearly with k.
Exploring undercurrents of learning tensions in an LLM-enhanced landscape: A student-centered qualitative perspective on LLM vs Search
Venue: arXiv cs.HC 2025
Year: 2025

Large language models (LLMs) are transforming how students learn by providing readily available tools that can quickly augment or complete various learning activities with non-trivial performance. Similar paradigm shifts have occurred in the past with the introduction of search engines and Wikipedia, which replaced or supplemented traditional information sources such as libraries and books. This study investigates the potential for LLMs to represent the next shift in learning, focusing on their role in information discovery and synthesis compared to existing technologies, such as search engines. Using a within-subjects, counterbalanced design, participants learned new topics using a search engine (Google) and an LLM (ChatGPT). Post-task follow-up interviews explored students' reflections, preferences, pain points, and overall perceptions. We present analysis of their responses that show nuanced insights into when, why, and how students prefer LLMs over search engines, offering implications for educators, policymakers, and technology developers navigating the evolving educational landscape.
Multi-Mission Tool Bench: Assessing the Robustness of LLM based Agents through Related and Dynamic Missions
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) demonstrate strong potential as agents for tool invocation due to their advanced comprehension and planning capabilities. Users increasingly rely on LLM-based agents to solve complex missions through iterative interactions. However, existing benchmarks predominantly access agents in single-mission scenarios, failing to capture real-world complexity. To bridge this gap, we propose the Multi-Mission Tool Bench. In the benchmark, each test case comprises multiple interrelated missions. This design requires agents to dynamically adapt to evolving demands. Moreover, the proposed benchmark explores all possible mission-switching patterns within a fixed mission number. Specifically, we propose a multi-agent data generation framework to construct the benchmark. We also propose a novel method to evaluate the accuracy and efficiency of agent decisions with dynamic decision trees. Experiments on diverse open-source and closed-source LLMs reveal critical factors influencing agent robustness and provide actionable insights to the tool invocation society.
A Framework for Developing University Policies on Generative AI Governance: A Cross-national Comparative Study
Venue: arXiv cs.CY 2025
Year: 2025

As generative AI (GAI) becomes more integrated into higher education, universities are actively exploring its governance and issuing guidelines to promote responsible use, reflecting varied stages of adoption and orientations. This study undertakes a comparative analysis of current GAI guidelines issued by leading universities in the United States, Japan, and China. Based on these findings, the study proposes a University Policy Development Framework for GAI (UPDF-GAI) to provide both theoretical insights and practical guidance for universities in developing and refining their GAI policies. This study adopts five domains from the extended Technology Acceptance Model. A qualitative content analysis of 124 policy documents from 110 universities was conducted, employing thematic coding to synthesize 20 key themes. These domains and themes form the foundation of the UPDF-GAI framework. The analysis reveals varying priorities and focus of GAI policy of universities in different countries. U.S. universities emphasize faculty autonomy, practical application, and policy adaptability, shaped by cutting-edge research and peer collaboration. Japanese universities take a government-regulated approach, prioritizing ethics and risk management, but provide limited support for AI implementation and flexibility. Chinese universities follow a centralized, government-led model, focusing on technology application over early policy development, while actively exploring GAI integration in education and research. The framework facilitates universities in formulating GAI policies by balancing its values and risks, providing multi-level support, proactively responding to societal impacts, and strengthening self-efficacy. In doing so, it enables the development of sustainable and context-sensitive policies that enhance digital competitiveness and advance preparedness for AI-driven education.
Prompt Optimization with Logged Bandit Data
Venue: arXiv cs.LG 2025
Year: 2025

We study how to use naturally available user feedback, such as clicks, to optimize large language model (LLM) pipelines for generating personalized sentences using prompts. Naive approaches, which estimate the policy gradient in the prompt space, suffer either from variance caused by the large action space of prompts or bias caused by inaccurate reward predictions. To circumvent these challenges, we propose a novel kernel-based off-policy gradient method, which estimates the policy gradient by leveraging similarity among generated sentences, substantially reducing variance while suppressing the bias. Empirical results on our newly established suite of benchmarks demonstrate the effectiveness of the proposed approach in generating personalized descriptions for movie recommendations, particularly when the number of candidate prompts is large.
SymDQN: Symbolic Knowledge and Reasoning in Neural Network-based Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

We propose a learning architecture that allows symbolic control and guidance in reinforcement learning with deep neural networks. We introduce SymDQN, a novel modular approach that augments the existing Dueling Deep Q-Networks (DuelDQN) architecture with modules based on the neuro-symbolic framework of Logic Tensor Networks (LTNs). The modules guide action policy learning and allow reinforcement learning agents to display behaviour consistent with reasoning about the environment. Our experiment is an ablation study performed on the modules. It is conducted in a reinforcement learning environment of a 5x5 grid navigated by an agent that encounters various shapes, each associated with a given reward. The underlying DuelDQN attempts to learn the optimal behaviour of the agent in this environment, while the modules facilitate shape recognition and reward prediction. We show that our architecture significantly improves learning, both in terms of performance and the precision of the agent. The modularity of SymDQN allows reflecting on the intricacies and complexities of combining neural and symbolic approaches in reinforcement learning.
Development of Automated Data Quality Assessment and Evaluation Indices by Analytical Experience
Venue: arXiv cs.HC 2025
Year: 2025

The societal need to leverage third-party data has driven the data-distribution market and increased the importance of data quality assessment (DQA) in data transactions between organizations. However, DQA requires expert knowledge of raw data and related data attributes, which hinders consensus-building in data purchasing. This study focused on the differences in DQAs between experienced and inexperienced data handlers. We performed two experiments: The first was a questionnaire survey involving 41 participants with varying levels of data-handling experience, who evaluated 12 data samples using 10 predefined indices with and without quality metadata generated by the automated tool. The second was an eye-tracking experiment to reveal the viewing behavior of participants during data evaluation. It was revealed that using quality metadata generated by the automated tool can reduce misrecognition in DQA. While experienced data handlers rated the quality metadata highly, semi-experienced users gave it the lowest ratings. This study contributes to enhancing data understanding within organizations and promoting the distribution of valuable data by proposing an automated tool to support DQAs.
Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole
Venue: arXiv cs.CL 2025
Year: 2025

We introduce a new dataset for machine translation of Guinea-Bissau Creole (Kiriol), comprising around 40 thousand parallel sentences to English and Portuguese. This dataset is made up of predominantly religious data (from the Bible and texts from the Jehovah's Witnesses), but also a small amount of general domain data (from a dictionary). This mirrors the typical resource availability of many low resource languages. We train a number of transformer-based models to investigate how to improve domain transfer from religious data to a more general domain. We find that adding even 300 sentences from the target domain when training substantially improves the translation performance, highlighting the importance and need for data collection for low-resource languages, even on a small-scale. We additionally find that Portuguese-to-Kiriol translation models perform better on average than other source and target language pairs, and investigate how this relates to the morphological complexity of the languages involved and the degree of lexical overlap between creoles and lexifiers. Overall, we hope our work will stimulate research into Kiriol and into how machine translation might better support creole languages in general.
Cybersickness Assessment Framework(TestBed): Towards a Standardization of Experiments
Venue: arXiv cs.HC 2025
Year: 2025

Investigating cybersickness (CS) in virtual reality (VR) often requires significant resources to create the VR environment and manage other experiment-related aspects. Additionally, slight differences in VR content across studies can lead to conflicting results. To address these challenges, we propose a standardized assessment framework to facilitate cybersickness research. The main goal is to enable consistent and comparable CS-related experiments. By establishing this common foundation, researchers can better evaluate and compare the impact of various factors on cybersickness. We provide a comprehensive explanation of the conceptual designs, detail the technical implementation, and offer instructions for using the proposed framework. Lastly, we conclude by discussing the limitations and potential avenues for future development.
The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual context
Venue: arXiv cs.CL 2025
Year: 2025

Alignment tuning has enabled large language models to excel in reasoning, instruction-following, and minimizing harmful generations. However, despite their widespread deployment, these models exhibit a monolingual bias, raising concerns about the effectiveness of alignment across languages. Current alignment methods predominantly focus on English, leaving it unclear how alignment mechanism generalize to multilingual settings. To address this, we conduct a systematic analysis of distributional shifts in the embedding space of LLMs before and after alignment, uncovering its impact on model behavior across diverse languages. We leverage the alignment-induced separation in safety space as a quantitative tool to measure how alignment enforces safety constraints. Our study evaluates seven LLMs using balanced toxicity datasets and parallel text-detoxification benchmarks, revealing substantial disparities in the latent representation space between high-resource and low-resource languages. These findings underscore the need for language-specific fine-tuning to ensure fair, reliable and robust multilingual alignment. Our insights provide a foundation for developing truly safe multilingual LLMs, emphasizing the urgency of addressing alignment gaps in underrepresented languages.
TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models
Venue: arXiv cs.IT 2025
Year: 2025

Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Telecom) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coordination. To address this issue, we propose Telecom mixture of models (TeleMoM), a consensus-driven ensemble framework that integrates multiple LLMs for enhanced decision-making in Telecom. TeleMoM employs a two-stage process: proponent models generate justified responses, and an adjudicator finalizes decisions, supported by a quality-checking mechanism. This approach leverages strengths of diverse models to improve accuracy, reduce biases, and handle domain-specific complexities effectively. Evaluation results demonstrate that TeleMoM achieves a 9.7\% increase in answer accuracy, highlighting its effectiveness in Telecom applications.
Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact
Venue: arXiv cs.HC 2025
Year: 2025

Photoplethysmography (PPG) is a widely adopted, non-invasive technique for monitoring cardiovascular health and physiological parameters in both consumer and clinical settings. While motion artifacts in dynamic environments have been extensively studied, suboptimal skin-sensor contact in sedentary conditions - a critical yet underexplored issue - can distort PPG waveform morphology, leading to the loss or misalignment of key features and compromising sensing accuracy. In this work, we propose CP-PPG, a novel framework that transforms Contact Pressure-distorted PPG signals into high-fidelity waveforms with ideal morphology. CP-PPG integrates a custom data collection protocol, a carefully designed signal processing pipeline, and a novel deep adversarial model trained with a custom PPG-aware loss function. We validated CP-PPG through comprehensive evaluations, including 1) morphology transformation performance on our self-collected dataset, 2) downstream physiological monitoring performance on public datasets, and 3) in-the-wild study. Extensive experiments demonstrate substantial and consistent improvements in signal fidelity (Mean Absolute Error: 0.09, 40% improvement over the original signal) as well as downstream performance across all evaluations in Heart Rate (HR), Heart Rate Variability (HRV), Respiration Rate (RR), and Blood Pressure (BP) estimation (on average, 21% improvement in HR; 41-46% in HRV; 6% in RR; and 4-5% in BP). These findings highlight the critical importance of addressing skin-sensor contact issues to enhance the reliability and effectiveness of PPG-based physiological monitoring. CP-PPG thus holds significant potential to improve the accuracy of wearable health technologies in clinical and consumer applications.
Faster Mixing of the Jerrum-Sinclair Chain
Venue: arXiv cs.DS 2025
Year: 2025

We show that the Jerrum-Sinclair Markov chain on matchings mixes in time $\widetilde{O}(Δ^2 m)$ on any graph with $n$ vertices, $m$ edges, and maximum degree $Δ$, for any constant edge weight $λ>0$. For general graphs with arbitrary, potentially unbounded $Δ$, this provides the first improvement over the classic $\widetilde{O}(n^2 m)$ mixing time bound of Jerrum and Sinclair (1989) and Sinclair (1992). To achieve this, we develop a general framework for analyzing mixing times, combining ideas from the classic canonical path method with the "local-to-global" approaches recently developed in high-dimensional expanders, introducing key innovations to both techniques.
Atrial constitutive neural networks
Venue: arXiv cs.CE 2025
Year: 2025

This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.